• Energy Research

The use of dynamic resistance–capacitance (RC) grey-box models to estimate building thermal energy demands is a well-known strategy. Nonetheless, there are no clear methodologies to generate model variations without having to re-identify the parameters that are simple, flexible, accurate and low computation demanding. This research introduces a simple methodology that combines parameter identification from white box generated data and simplified theoretical building thermal equations to create functional and reliable RC model variations from an original grey-box model. It demonstrates that the original RC grey-box parameters can be altered based on theoretical value variation factors to represent different building renovation scenarios accurately. The study provides an overview of the grey-box modelling process (R2C2 model), highlighting the flexibility of RC parameters and their impact on building thermal behaviour. The initially generated grey-box model parameters are adapted to various scenarios, involving common building envelope alterations that are seen in renovations. The thermal energy demands from the adjusted grey-box model are compared with those from a detailed white-box model, confirming the reliability of the proposed methodology. The results prove the adaptability and robustness of the R2C2 parameters, enabling a new strategy that reduces modelling complexity and time while maintaining high accuracy. This advancement offers a fresh perspective for creating large-scale building retrofitting models, urban simulation tools, microclimate studies, renewable energy integration, predictive control models, and other applications requiring simplified, rapid and reliable simulations.